{
 "cells": [
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   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-03-07T13:01:58.683534Z",
     "start_time": "2025-03-07T13:01:58.553734Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "df = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
    "df.head()\n",
    "df_Origin_data = df.copy()\n",
    "df_Origin_data.head()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0   9046    Male  67.0             0              1          Yes   \n",
       "1  51676  Female  61.0             0              0          Yes   \n",
       "2  31112    Male  80.0             0              1          Yes   \n",
       "3  60182  Female  49.0             0              0          Yes   \n",
       "4   1665  Female  79.0             1              0          Yes   \n",
       "\n",
       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0        Private          Urban             228.69  36.6  formerly smoked   \n",
       "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2        Private          Rural             105.92  32.5     never smoked   \n",
       "3        Private          Urban             171.23  34.4           smokes   \n",
       "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
       "\n",
       "   stroke  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ],
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       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
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       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-07T13:01:58.740366Z",
     "start_time": "2025-03-07T13:01:58.732945Z"
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   },
   "cell_type": "code",
   "source": [
    "label_encoder = LabelEncoder()\n",
    "for column in df.select_dtypes(include=['object']).columns:\n",
    " df[column] = label_encoder.fit_transform(df[column])\n",
    "df.head()"
   ],
   "id": "495e889bf2a605a1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease  ever_married  work_type  \\\n",
       "0   9046       1  67.0             0              1             1          2   \n",
       "1  51676       0  61.0             0              0             1          3   \n",
       "2  31112       1  80.0             0              1             1          2   \n",
       "3  60182       0  49.0             0              0             1          2   \n",
       "4   1665       0  79.0             1              0             1          3   \n",
       "\n",
       "   Residence_type  avg_glucose_level   bmi  smoking_status  stroke  \n",
       "0               1             228.69  36.6               1       1  \n",
       "1               0             202.21   NaN               2       1  \n",
       "2               0             105.92  32.5               2       1  \n",
       "3               1             171.23  34.4               3       1  \n",
       "4               0             174.12  24.0               2       1  "
      ],
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      ]
     },
     "execution_count": 2,
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    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-07T13:01:58.759660Z",
     "start_time": "2025-03-07T13:01:58.756303Z"
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   },
   "cell_type": "code",
   "source": "df.isnull().sum()",
   "id": "c367200e1397dc28",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     0\n",
       "gender                 0\n",
       "age                    0\n",
       "hypertension           0\n",
       "heart_disease          0\n",
       "ever_married           0\n",
       "work_type              0\n",
       "Residence_type         0\n",
       "avg_glucose_level      0\n",
       "bmi                  201\n",
       "smoking_status         0\n",
       "stroke                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "as 201 / 5110 is less than 5% no need to remove the column",
   "id": "a489e0ee3df61df6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-07T13:01:58.913269Z",
     "start_time": "2025-03-07T13:01:58.829162Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# filling nulls \n",
    "imputer = KNNImputer(n_neighbors=2)\n",
    "df_no_nulls = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)\n",
    "df_no_nulls.head()"
   ],
   "id": "1237fd3caec9989d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        id  gender   age  hypertension  heart_disease  ever_married  \\\n",
       "0   9046.0     1.0  67.0           0.0            1.0           1.0   \n",
       "1  51676.0     0.0  61.0           0.0            0.0           1.0   \n",
       "2  31112.0     1.0  80.0           0.0            1.0           1.0   \n",
       "3  60182.0     0.0  49.0           0.0            0.0           1.0   \n",
       "4   1665.0     0.0  79.0           1.0            0.0           1.0   \n",
       "\n",
       "   work_type  Residence_type  avg_glucose_level    bmi  smoking_status  stroke  \n",
       "0        2.0             1.0             228.69  36.60             1.0     1.0  \n",
       "1        3.0             0.0             202.21  30.25             2.0     1.0  \n",
       "2        2.0             0.0             105.92  32.50             2.0     1.0  \n",
       "3        2.0             1.0             171.23  34.40             3.0     1.0  \n",
       "4        3.0             0.0             174.12  24.00             2.0     1.0  "
      ],
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       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
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       "      <td>3.0</td>\n",
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       "      <td>24.00</td>\n",
       "      <td>2.0</td>\n",
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      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-07T13:01:59.004112Z",
     "start_time": "2025-03-07T13:01:58.976001Z"
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   },
   "cell_type": "code",
   "source": [
    "data=df_no_nulls.copy()\n",
    "filtered_df=df_no_nulls.copy()\n",
    "\n",
    "def detecting_outliers(column):\n",
    " Q1 = column.quantile(0.25)\n",
    " Q3 = column.quantile(0.75)\n",
    " IQR = Q3 - Q1\n",
    " lower_bound = Q1 - 1.5 * IQR\n",
    " upper_bound = Q3 + 1.5 * IQR\n",
    " # Return a boolean series indicating if the value is an outlier\n",
    " return (column < lower_bound) | (column > upper_bound)\n",
    "# Apply the IQR method to each feature in the DataFrame\n",
    "for col in data.columns:\n",
    " data[f'{col}_Outlier'] = detecting_outliers(data[col])\n",
    " print(data[f'{col}_Outlier'].value_counts())\n",
    "# Filter the DataFrame to display rows where any outlier flag is True\n",
    "outliers_flag = data[data.filter(like='_Outlier').any(axis=1)]\n",
    "# Display the filtered DataFrame with outliers\n",
    "outliers_flag.head()"
   ],
   "id": "6be84de41869bf6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id_Outlier\n",
      "False    5110\n",
      "Name: count, dtype: int64\n",
      "gender_Outlier\n",
      "False    5110\n",
      "Name: count, dtype: int64\n",
      "age_Outlier\n",
      "False    5110\n",
      "Name: count, dtype: int64\n",
      "hypertension_Outlier\n",
      "False    4612\n",
      "True      498\n",
      "Name: count, dtype: int64\n",
      "heart_disease_Outlier\n",
      "False    4834\n",
      "True      276\n",
      "Name: count, dtype: int64\n",
      "ever_married_Outlier\n",
      "False    5110\n",
      "Name: count, dtype: int64\n",
      "work_type_Outlier\n",
      "False    4453\n",
      "True      657\n",
      "Name: count, dtype: int64\n",
      "Residence_type_Outlier\n",
      "False    5110\n",
      "Name: count, dtype: int64\n",
      "avg_glucose_level_Outlier\n",
      "False    4483\n",
      "True      627\n",
      "Name: count, dtype: int64\n",
      "bmi_Outlier\n",
      "False    4995\n",
      "True      115\n",
      "Name: count, dtype: int64\n",
      "smoking_status_Outlier\n",
      "False    5110\n",
      "Name: count, dtype: int64\n",
      "stroke_Outlier\n",
      "False    4861\n",
      "True      249\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "        id  gender   age  hypertension  heart_disease  ever_married  \\\n",
       "0   9046.0     1.0  67.0           0.0            1.0           1.0   \n",
       "1  51676.0     0.0  61.0           0.0            0.0           1.0   \n",
       "2  31112.0     1.0  80.0           0.0            1.0           1.0   \n",
       "3  60182.0     0.0  49.0           0.0            0.0           1.0   \n",
       "4   1665.0     0.0  79.0           1.0            0.0           1.0   \n",
       "\n",
       "   work_type  Residence_type  avg_glucose_level    bmi  ...  age_Outlier  \\\n",
       "0        2.0             1.0             228.69  36.60  ...        False   \n",
       "1        3.0             0.0             202.21  30.25  ...        False   \n",
       "2        2.0             0.0             105.92  32.50  ...        False   \n",
       "3        2.0             1.0             171.23  34.40  ...        False   \n",
       "4        3.0             0.0             174.12  24.00  ...        False   \n",
       "\n",
       "   hypertension_Outlier  heart_disease_Outlier  ever_married_Outlier  \\\n",
       "0                 False                   True                 False   \n",
       "1                 False                  False                 False   \n",
       "2                 False                   True                 False   \n",
       "3                 False                  False                 False   \n",
       "4                  True                  False                 False   \n",
       "\n",
       "   work_type_Outlier  Residence_type_Outlier  avg_glucose_level_Outlier  \\\n",
       "0              False                   False                       True   \n",
       "1              False                   False                       True   \n",
       "2              False                   False                      False   \n",
       "3              False                   False                       True   \n",
       "4              False                   False                       True   \n",
       "\n",
       "   bmi_Outlier  smoking_status_Outlier  stroke_Outlier  \n",
       "0        False                   False            True  \n",
       "1        False                   False            True  \n",
       "2        False                   False            True  \n",
       "3        False                   False            True  \n",
       "4        False                   False            True  \n",
       "\n",
       "[5 rows x 24 columns]"
      ],
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>...</th>\n",
       "      <th>age_Outlier</th>\n",
       "      <th>hypertension_Outlier</th>\n",
       "      <th>heart_disease_Outlier</th>\n",
       "      <th>ever_married_Outlier</th>\n",
       "      <th>work_type_Outlier</th>\n",
       "      <th>Residence_type_Outlier</th>\n",
       "      <th>avg_glucose_level_Outlier</th>\n",
       "      <th>bmi_Outlier</th>\n",
       "      <th>smoking_status_Outlier</th>\n",
       "      <th>stroke_Outlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.60</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.21</td>\n",
       "      <td>30.25</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.50</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.40</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.00</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-07T13:01:59.129448Z",
     "start_time": "2025-03-07T13:01:59.128090Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "b7193b49a9439e29",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-07T13:01:59.427180Z",
     "start_time": "2025-03-07T13:01:59.162556Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(12, 6))\n",
    "df_outlier =  df[['hypertension','work_type','avg_glucose_level','bmi','stroke']]\n",
    "for i, col in enumerate(df_outlier.columns): # Only plotting the first 5 features\n",
    " plt.subplot(1, 5, i+1) # Create subplots for each feature\n",
    " sns.boxplot(x=df[col])\n",
    " plt.title(f\"Boxplot for {col}\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "f511970ceb8fb27e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 5 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Data Visulization",
   "id": "1eb75207d8041a9b"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "12020c1c40340f03"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-07T13:01:59.466218Z",
     "start_time": "2025-03-07T13:01:59.451540Z"
    }
   },
   "cell_type": "code",
   "source": "df_no_nulls.describe()",
   "id": "e54b3db8afd76434",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 id       gender          age  hypertension  heart_disease  \\\n",
       "count   5110.000000  5110.000000  5110.000000   5110.000000    5110.000000   \n",
       "mean   36517.829354     0.414286    43.226614      0.097456       0.054012   \n",
       "std    21161.721625     0.493044    22.612647      0.296607       0.226063   \n",
       "min       67.000000     0.000000     0.080000      0.000000       0.000000   \n",
       "25%    17741.250000     0.000000    25.000000      0.000000       0.000000   \n",
       "50%    36932.000000     0.000000    45.000000      0.000000       0.000000   \n",
       "75%    54682.000000     1.000000    61.000000      0.000000       0.000000   \n",
       "max    72940.000000     2.000000    82.000000      1.000000       1.000000   \n",
       "\n",
       "       ever_married    work_type  Residence_type  avg_glucose_level  \\\n",
       "count   5110.000000  5110.000000     5110.000000        5110.000000   \n",
       "mean       0.656164     2.167710        0.508023         106.147677   \n",
       "std        0.475034     1.090293        0.499985          45.283560   \n",
       "min        0.000000     0.000000        0.000000          55.120000   \n",
       "25%        0.000000     2.000000        0.000000          77.245000   \n",
       "50%        1.000000     2.000000        1.000000          91.885000   \n",
       "75%        1.000000     3.000000        1.000000         114.090000   \n",
       "max        1.000000     4.000000        1.000000         271.740000   \n",
       "\n",
       "               bmi  smoking_status       stroke  \n",
       "count  5110.000000     5110.000000  5110.000000  \n",
       "mean     28.958395        1.376908     0.048728  \n",
       "std       7.794172        1.071534     0.215320  \n",
       "min      10.300000        0.000000     0.000000  \n",
       "25%      23.612500        0.000000     0.000000  \n",
       "50%      28.100000        2.000000     0.000000  \n",
       "75%      33.100000        2.000000     0.000000  \n",
       "max      97.600000        3.000000     1.000000  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36517.829354</td>\n",
       "      <td>0.414286</td>\n",
       "      <td>43.226614</td>\n",
       "      <td>0.097456</td>\n",
       "      <td>0.054012</td>\n",
       "      <td>0.656164</td>\n",
       "      <td>2.167710</td>\n",
       "      <td>0.508023</td>\n",
       "      <td>106.147677</td>\n",
       "      <td>28.958395</td>\n",
       "      <td>1.376908</td>\n",
       "      <td>0.048728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21161.721625</td>\n",
       "      <td>0.493044</td>\n",
       "      <td>22.612647</td>\n",
       "      <td>0.296607</td>\n",
       "      <td>0.226063</td>\n",
       "      <td>0.475034</td>\n",
       "      <td>1.090293</td>\n",
       "      <td>0.499985</td>\n",
       "      <td>45.283560</td>\n",
       "      <td>7.794172</td>\n",
       "      <td>1.071534</td>\n",
       "      <td>0.215320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.120000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17741.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.245000</td>\n",
       "      <td>23.612500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36932.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>91.885000</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54682.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>114.090000</td>\n",
       "      <td>33.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72940.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>271.740000</td>\n",
       "      <td>97.600000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-07T13:01:59.556707Z",
     "start_time": "2025-03-07T13:01:59.486975Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x='gender', data=df_Origin_data)\n",
    "plt.title('Gender Count')\n",
    "plt.xlabel('Classification')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ],
   "id": "f03ead52098937af",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-07T13:01:59.669865Z",
     "start_time": "2025-03-07T13:01:59.582986Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(df_no_nulls['age'], bins=15, kde=True, color='blue')\n",
    "plt.title('Age Distribution')\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show() "
   ],
   "id": "204a1cc226eb501c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-07T13:01:59.788222Z",
     "start_time": "2025-03-07T13:01:59.692372Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x='ever_married', data=df_Origin_data)\n",
    "plt.title('Ever married Count')\n",
    "plt.xlabel('Classification')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ],
   "id": "1721f052c37c7900",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-07T13:01:59.909145Z",
     "start_time": "2025-03-07T13:01:59.808731Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(x='age', y='heart_disease', data=df_no_nulls,  )\n",
    "plt.title('Age vs Heart Disease')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Heart Disease')\n",
    "plt.show()"
   ],
   "id": "bf317fb6af26962a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "9d468c380cf0e60f"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "7159a5b23bfdbb33"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}